Functional pipelines

ABSTRACT

A system and method for employing multiple hardware contexts and programming engines in a functional pipeline partitioned to facilitate high performance data processing. The system and method includes a parallel processor that assigns system functions for processing data including programming engines that support multiple contexts arranged to provide a functional pipeline by a functional pipeline control unit that passes functional data among the programming engines.

BACKGROUND

[0001] This invention relates to functional pipelines.

[0002] Parallel processing is an efficient form of information processing of concurrent events of a computing system. Parallel processing demands concurrent execution of many programs, in contrast to sequential processing. In the context of parallel processing, parallelism involves doing more than one thing at the same time. Unlike a serial paradigm where all tasks are performed sequentially at a single station or a pipelined machine where tasks are performed at specialized stations, with parallel processing, many stations are provided, each capable of performing and carrying out various tasks and functions simultaneously. A number of stations work simultaneously and independently on the same or common elements of a computing task. Accordingly, parallel processing solves various types of computing tasks and certain problems are suitable for solution by applying several instruction processing units and several data streams.

BRIEF DESCRIPTION OF THE DRAWINGS

[0003]FIG. 1 is a block diagram of a processing system.

[0004]FIG. 2 is a detailed block diagram of the processing system of FIG. 1.

[0005]FIG. 3 is a block diagram of a programming engine of the processing system of FIG. 1.

[0006]FIG. 4 is a block diagram of a functional pipeline unit of the processing system of FIG. 1.

[0007]FIG. 5 is a block diagram illustrating details of the processing system of FIG. 1.

DESCRIPTION

[0008] Architecture:

[0009] Referring to FIG. 1, a computer processing system 10 includes a parallel, hardware-based multithreaded network processor 12. The hardware-based multithreaded processor 12 is coupled to a memory system or memory resource 14. Memory system 14 includes dynamic random access memory (DRAM) 14 a and static random access memory 14 b (SRAM). The processing system 10 is especially useful for tasks that can be broken into parallel subtasks or functions. Specifically, the hardware-based multithreaded processor 12 is useful for tasks that are bandwidth oriented rather than latency oriented. The hardware-based multithreaded processor 12 has multiple functional microengines or programming engines 16 each with multiple hardware controlled threads that are simultaneously active and independently work on a specific task.

[0010] The programming engines 16 each maintain program counters in hardware and states associated with the program counters. Effectively, corresponding sets of context or threads can be simultaneously active on each of the programming engines 16 while only one is actually operating at any one time.

[0011] In this example, eight programming engines 16 a-16 h are illustrated in FIG. 1. Each engine from the programming engines 16 a-16 h processes eight hardware threads or contexts. The eight programming engines 16 a-16 h operate with shared resources including memory resource 14 and bus interfaces (not shown). The hardware-based multithreaded processor 12 includes a dynamic random access memory (DRAM) controller 18 a and a static random access memory (SRAM) controller 18 b. The DRAM memory 14 a and DRAM controller 18 a are typically used for processing large volumes of data, e.g., processing of network payloads from network packets. The SRAM memory 14 b and SRAM controller 18 b are used in a networking implementation for low latency, fast access tasks, e.g., accessing look-up tables, memory for the core processor 20, and the like.

[0012] The eight programming engines 16 a-16 h access either the DRAM memory 14 a or SRAM memory 14 b based on characteristics of the data. Thus, low latency, low bandwidth data is stored in and fetched from SRAM memory 14 b, whereas higher bandwidth data for which latency is not as important, is stored in and fetched from DRAM memory 14 a. The programming engines 16 a-16 h can execute memory reference instructions to either the DRAM controller 18 a or SRAM controller 18 b.

[0013] The hardware-based multithreaded processor 12 also includes a processor core 20 for loading microcode control for the programming engines 16 a-16 h. In this example, the processor core 20 is an XScale™ based architecture.

[0014] The processor core 20 performs general purpose computer type functions such as handling protocols, exceptions, and extra support for packet processing where the programming engines 16 pass the packets off for more detailed processing such as in boundary conditions.

[0015] The processor core 20 has an operating system (not shown). Through the operating system (OS), the processor core 20 can call functions to operate on the programming engines 16 a-16 h. The processor core 20 can use any supported OS, in particular, a real time OS. For the core processor 20 implemented as an XScale™ architecture, operating systems such as Microsoft NT real-time, VXWorks and μCOS, or a freeware OS available over the Internet can be used.

[0016] Advantages of hardware multithreading can be explained by SRAM or DRAM memory accesses. As an example, an SRAM access requested by a context (e.g., Thread_(—)0), from one of the programming engines 16 will cause the SRAM controller 18 b to initiate an access to the SRAM memory 14 b. The SRAM controller 18 b accesses the SRAM memory 14 b, fetches the data from the SRAM memory 14 b, and returns data to a requesting programming engine 16.

[0017] During an SRAM access, if one of the programming engines 16 a-16 h had only a single thread that could operate, that programming engine would be dormant until data was returned from the SRAM memory 14 b.

[0018] By employing hardware context swapping within each of the programming engines 16 a-16 h, the hardware context swapping enables other contexts with unique program counters to execute in that same programming engine. Thus, another thread e.g., Thread_(—)1 can function while the first thread, Thread_(—)0, is awaiting the read data to return. During execution, Thread_(—)1 may access the DRAM memory 14 a. While Thread_(—)1 operates on the DRAM unit, and Thread_(—)0 is operating on the SRAM unit, a new thread, e.g., Thread_(—)2 can now operate in the programming engine 16. Thread_(—)2 can operate for a certain amount of time until it needs to access memory or perform some other long latency operation, such as making an access to a bus interface. Therefore, simultaneously, the multi-threaded processor 12 can have a bus operation, an SRAM operation, and a DRAM operation all being completed or operated upon by one of the programming engines 16 and have one more threads or contexts available to process more work.

[0019] The hardware context swapping also synchronizes the completion of tasks. For example, two threads can access the shared memory resource, e.g., the SRAM memory 14 b. Each one of the separate functional units, e.g., the SRAM controller 18 b, and the DRAM controller 18 a, when they complete a requested task from one of the programming engine threads or contexts reports back a flag signaling completion of an operation. When the programming engines 16 a-16 h receive the flag, the programming engines 16 a-16 h can determine which thread to turn on.

[0020] One example of an application for the hardware-based multithreaded processor 12 is as a network processor. As a network processor, the hardware-based multithreaded processor 12 interfaces to network devices such as a Media Access Controller (MAC) device, e.g., a 10/100BaseT Octal MAC 13 a or a Gigabit Ethernet device 13 b. In general, as a network processor, the hardware-based multithreaded processor 12 can interface to any type of communication device or interface that receives or sends large amount of data. The computer processing system 10 functioning in a networking application can receive network packets and process those packets in a parallel manner.

[0021] Programming Engine Contexts:

[0022] Referring to FIG. 2, one exemplary programming engine 16 a from the programming engines 16 a-16 h, is shown. The programming engine 16 a includes a control store 30, which in one example includes a RAM of 4096 instructions, each of which is 40-bits wide. The RAM stores a microprogram that the programming engine 16 a executes. The microprogram in the control store 30 is loadable by the processor core 20 (FIG. 1).

[0023] In addition to event signals that are local to an executing thread, the programming engine 16 a employs signaling states that are global. With signaling states, an executing thread can broadcast a signal state to all programming engines 16 a-16 h. Any and all threads in the programming engines can branch on these signaling states. These signaling states can be used to determine availability of a resource or whether a resource is due for servicing. The context event logic has arbitration for the eight (8) threads. In one example, the arbitration is a round robin mechanism. Other techniques could be used including priority queuing or weighted fair queuing.

[0024] As described above, the programming engine 16 a supports multi-threaded execution of eight contexts. This allows one thread to start executing just after another thread issues a memory reference and must wait until that reference completes before doing more work. Multi-threaded execution is critical to maintaining efficient hardware execution of the programming engine 16 a because memory latency is significant. Multi-threaded execution allows the programming engines 16 to hide memory latency by performing useful independent work across several threads.

[0025] The programming engine 16 a, to allow for efficient context swapping, has its own register set, program counter, and context specific local registers. Having a copy per context eliminates the need to move context specific information to and from shared memory and programming engine registers for each context swap. Fast context swapping allows a context to do computation while other contexts wait for input-output (I/O), typically, external memory accesses to complete or for a signal from another context or hardware unit.

[0026] For example, the programming engine 16 a executes the eight contexts by maintaining eight program counters and eight context relative sets of registers. There can be six different types of context relative registers, namely, general purpose registers (GPRs) 32, inter-programming agent registers (not shown), Static Random Access Memory (SRAM) input transfer registers 34, Dynamic Random Access Memory (DRAM) input transfer registers 36, SRAM output transfer registers 38, DRAM output transfer registers 40.

[0027] The GPRs 32 are used for general programming purposes. The GPRs 32 are read and written exclusively under program control. The GPRs 32, when used as a source in an instruction, supply operands to an execution datapath 44. When used as a destination in an instruction, the GPRs 32 are written with the result of the execution datapath 44. The programming engine 16 a also includes I/O transfer registers 34, 36, 38 and 40 which are used for transferring data to and from the programming engine 16 a and locations external to the programming engines 16 a, e.g., the DRAM memory 14 a, the SRAM memory 14 b, and etc.

[0028] A local memory 42 is also used. The local memory 42 is addressable storage located in the programming engine 16 a. The local memory 42 is read and written exclusively under program control. The local memory 42 also includes variables shared by all the programming engines 16 a-16 h. Shared variables are modified in various assigned tasks during functional pipeline stages by the programming engines 16 a-16 h, which are described next. The shared variables include a critical section, defining the read-modify-write times. The implementation and use of the critical section in the computing processing system 10 is also described below.

[0029] Functional Pipelining and Pipeline Stages:

[0030] Referring to FIG. 3, the programming engine 16 a is shown in a functional pipeline unit 50. The functional pipeline unit 50 includes the programming engine 16 a and a data unit 52 that includes data, operated on by the programming engine, e.g., network packets 54. The programming engine 16 a is shown having a local register unit 56. The local register unit 56 stores information from the data packets 54.

[0031] In the functional pipeline unit 50, the contexts 58 of the programming engines 16 a, namely, Programming Engine0.1 (PE0.1) through Programming Engine0.n (PE0.n), remain with the programming engine 16 a while different functions are performed on the data packets 54 as time 66 progresses from time=0 to time=t. A programming execution time is divided into “m” functional pipeline stages or pipe-stages 60 a-60 m. Each pipeline stage of the pipeline stages 60 a-60 m performs different pipeline functions 62 a, 64, or 62 p on data in the pipeline.

[0032] The pipeline stage 60 a is, for example, a regular time interval within which a particular processing function, e.g., the function 62 is applied to one of the data packets 54. A processing function 62 can last one or more pipelines stages 62.

[0033] The function 64, for example, lasts two pipeline stages, namely pipeline stages 60 b and 60 c.

[0034] A single programming engine such as the programming engine 16 a can constitute a functional pipeline unit 50. In the functional pipeline unit 50, the functions 62 a, 64, and 62 p move through the functional pipeline unit 50 from one programming engine 16 to another programming engine 16, as will be described next.

[0035] Referring to FIG. 4, the data packets 54 are assigned to programming engine contexts 58 in order. Thus, if “n” threads or contexts 58 execute in the programming engine 16 a, the first context 58, “PE0.1” completes processing of the data packet 54 before the data packets 54 from the “PE0.n” context arrives. With this approach the programming engine 16 b can begin processing the “n+1” packet.

[0036] Dividing the execution time of the programming engines 16 into functional pipeline stages 60 a-60 c results in more than one programming engine 16 executing an equivalent functional pipeline unit 70 in parallel. The functional pipeline stage 60 a is distributed across two programming engines 16 a and 16 b, with each of the programming engines 16 a and 16 b executing eight contexts each.

[0037] In operation, each of the data packets 54 remains with one of the contexts 58 for a longer period of time as more programming engines 16 are added to the functional pipeline units 50 and 70. In this example, the data packet 54 remains with a context sixteen data packet arrival times (8 contexts×2 programming engines) because context PE0.1 is not required to accept another data packet 58 until the other contexts 58 have received their data packets.

[0038] In this example, the function 62 of the functional pipeline stage 60 a can be passed from the programming engine 16 a to the programming engine 16 b. Passing of the function 62 is accomplished by passing the processing functions from one programming engine to another, as illustrated by dotted lines 80 a-80 c in FIG. 4.

[0039] The number of functional pipeline stages 60 a-60 m is equal to the number of the programming engines 16 a and 16 b in the functional pipeline units 50 and 70. This ensures that a particular pipeline stage executes in only one programming engine 16 at any one time.

[0040] Referring to FIG. 5, functional pipeline units 50, 70, and 90 are shown to include the programming engines 16 a (PE0), 16 b (PE1), and 16 c (PE2), respectively, in addition to the data units 52 a-52 c. Between the programming engines 16 a-16 c, critical sections 82 a-82 c and 84 a-84 c are provided. The critical sections 82 a-82 c and 84 a-84 c are used in conjunction with shared data 86 a-86 c and 88 a-88 c.

[0041] In the critical sections 82 a-82 c and 84 a-84 c, the programming engine contexts 58 a-58 c are given exclusive access to the shared data 86 a-86 c (e.g., cyclic redundancy check residue (CRC), reassembly context, or a statistic) in external memory.

[0042] In operation, functions can be distributed across one or more functional pipeline stages 60 a-60 d. For example, the critical section 82 a represents a section of code where only one programming engine context, in this case, the context 58 a of the programming engine 16 a, has exclusive modification privileges for a global resource (i.e., shared data 86 a), such as a location in memory, at any one time. Thus, the critical section 82 a provides exclusive modification privileges to a particular functional pipeline stage of the programming engine 16 a. The critical section 82 a also provides support for exclusive access between the contexts 58 a-58 c in the programming engines 16 a-16 c.

[0043] In certain implementations only one function modifies the shared data 86 a-86 c between the programming engines 16 a-16 c, to ensure exclusive modification privileges between the programming engines 16 a-16 c. The function that modifies the shared data 86 a-86 c executes, e.g., in a single functional pipeline stage 60 a, and the functional pipeline unit 50 is designed so that only one programming engine from all the programming engines 16 a-16 c executes the functional pipeline stage 60 a at any one time.

[0044] Still referring to FIG. 5, each of the programming engines 16 a-16 c is assigned exclusive modification privileges to the non-shared data 86 a-86 c and 88 a-88 c, satisfying the requirement that only one function modifies the non-shared data 86 a-86 c and 88 a-88 c between the programming engines 16 a-16 c.

[0045] In this example, by optimizing the control flow through the functional pipeline units 50, 70, and 90, the architectural solution described above presents greater network processing power to the hardware-based multithreaded network processor 12.

[0046] In the functional pipeline unit 50, the programming engine 16 a transitions into the critical section 82 a of the functional pipeline stage 60 a unless it can be assured that its “next” programming engine 16 b has transitioned out of the critical section 82 a. The programming engine 16 b uses inter-thread signaling where the signaling states can be used to determine availability of a memory resource or whether a memory resource is due for servicing. There are four ways to signal a next context using inter-thread signaling: Thread Signaling Mechanism 1. Signal next thread in the same PE Local Control and Status Registers (CSR) write 2. Signal a specific thread in the Local CSR write same PE 3. Signal the thread in the next PE Local CSR write 4. Signal any thread in an PE CSR write

[0047] Critical sections such as CRC calculations are performed in the order of incoming data packets 54 a-54 c because inter-thread signaling is performed in order.

[0048] When the functional pipeline stage 60 a transition occurs between the programming engines 16 a and 16 b, the elasticity buffer 92, implemented as a ring, is used. Each functional pipeline stage 60 a is implemented to execute within the time allocated to the functional pipeline stage 60 a. However, the elasticity buffer 92 accommodates jitter in the execution of the functional pipeline unit 50. Thus, if a functional pipeline stage 60 a falls behind in execution due to system anomalies such as high utilization of memory units over a short period of time, the elasticity buffer 92 allows the context 58 a of the functional pipeline stage 60 a to be buffered so that the previous functional pipeline stages will not be stalled waiting for the next pipeline stage to complete. The elasticity buffer 92 as shown in FIG. 5 also allows different heartbeats to the different functional pipelines units 70 and 90.

[0049] By using the functional pipeline stages, as described above, the functional pipeline units 50, 70, and 90 cover memory latency and provides sufficient computation cycles for data packets 54 arriving faster than a single stream computation stage. By providing a mechanism for fast synchronization from one programming engine to the next programming engine which performs the same set of functions on a new set of data packets 54, parallel processing capability of the programming engines are greatly enhanced. Multiple means for passing functional state and control is thus provided.

[0050] Other Embodiments:

[0051] In the example described above in conjunction with FIGS. 1-5, the computer processing system 10 may implement programming engines 16 using a family of network processors, namely, Intel Network Processor Family chips designed by Intel® Corporation, of Santa Clara, Calif.

[0052] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims. 

What is claimed is:
 1. A system comprising: a parallel processor that assigns system functions for processing data including a plurality of programming engines that support multiple contexts arranged to provide a functional pipeline by a functional pipeline control unit that passes functional data among the plurality of programming engines.
 2. The system of claim 1 further comprising: a synchronization unit across the functional pipeline unit.
 3. The system of claim 1 wherein the functional pipeline unit includes a plurality of functional pipeline stages.
 4. The system of claim 3 wherein the plurality of programming engines have an execution time for processing a task and the execution time is partitioned into a number of time intervals corresponding to the number of the plurality of functional pipeline stages.
 5. The system of claim 4 wherein each of the plurality of functional pipeline stages perform a different system function.
 6. The system of claim 1 wherein at least one of the plurality of programming engines is the functional pipeline unit.
 7. The system of claim 1 wherein the plurality of programming engines are configured to process a data packet in order.
 8. The system of claim 7 wherein the data packet are assigned to the multiple contexts of the plurality of programming engines.
 9. The system of claim 1 wherein the plurality of programming engines are configured to execute a data packet processing function using the functional pipeline unit of the system.
 10. The system of claim 9 wherein a data packet is maintained in the plurality of programming engines for a period of time corresponding to the number of the plurality of programming engines.
 11. The system of claim 3 wherein the number of the plurality of pipeline stages is equal to the number of the plurality of programming engines.
 12. The system of claim 3 wherein the plurality of pipeline stages include a critical section.
 13. The system of claim 12 wherein the critical section provides exclusive access for the multiple contexts to non-shared data required for processing data packets.
 14. The system of claim 3 wherein the plurality of programming engines include inter-thread signaling.
 15. The system of claim 3 wherein the plurality of programming engines include an elasticity buffer that accommodates jitter between the plurality of pipeline stages upon execution of a data packet processing function.
 16. A method of transferring data between a plurality of programming engines, the method comprising: assigning system functions for processing data in a parallel processor to corresponding ones of a plurality of programming engines that provide a functional pipeline unit, which supports execution of multiple contexts in each of the plurality of programming engines; and passing functional data among the plurality of programming engines in the functional pipeline unit.
 17. The method of claim 16 further comprising synchronizing the system functions across the functional pipeline unit.
 18. The method of claim 17 further comprising partitioning an execution time into a number of time intervals corresponding to the number of plurality of pipeline stages.
 19. The method of claim 16 wherein the plurality of programming engines use multiple contexts to process the data packet in order.
 20. The method of claim 16 wherein the plurality of programming engines execute a data packet processing functions using the functional pipeline unit of the system.
 21. The method of claim 16 further comprising using a critical section that provides exclusive access for the multiple contexts to non-shared data required for processing data packets.
 22. The method of claim 16 further comprising employing an elasticity buffer to accommodate jitter between the plurality of pipeline stages upon execution of a data packet processing function.
 23. A computer program product residing on a computer readable medium for causing a parallel processor to perform a function comprises instructions causing the processor to: assign system functions for processing data in a parallel processor to corresponding ones of a plurality of programming engines that provide a functional pipeline unit, which supports execution of multiple contexts in each of the plurality of programming engines; and pass functional data among the plurality of programming engines in the functional pipeline unit.
 24. The computer program product of claim 23 further comprising instructions causing the processor to synchronize the system functions across the functional pipeline unit.
 25. The computer program product of claim 23 wherein the plurality of programming engines execute a data packet processing functions using the functional pipeline unit of the system. 